Nowadays technical systems often exhibit a high level of complexity, i.e. they are described by states with a plurality of state variables. The state variables in this case are especially measurable state values of the technical system, e.g. physical values such as pressure, temperature, power and the like. In the control of complex technical systems computer-aided methods are often employed which optimize the dynamic timing behavior of the technical system while taking into account predetermined criteria. Examples of such methods are learning methods such as reinforcement learning methods sufficiently well known from the prior art (see document [2]). These methods optimize the dynamic behavior of the technical system by defining suitable actions to be executed on the technical system, with these actions comprising modifications of specific manipulated variables on the technical system, such as changing valve positions, increasing pressures and the like for example. Each action in this case is evaluated in a suitable manner by reward and punishment, for example by including a cost function, by which an optimal dynamic behavior of the technical system can be achieved.
With the standard method described above for control or optimization of the dynamic behavior of technical systems, the problem arising is that such methods can only be used to a limited extent for states with a plurality of state variables (i.e. in a high-dimensional state space).
Known from the prior art so-called methods for “feature selection” with which state spaces can be reduced. However such cases as a rule only a selection of the relevant state variables and not a reduction of the dimension of the state space on the basis of all variables is undertaken. In addition these variables are static and do not carry out any explicit observation and identification of the dynamic behavior of the technical system.
In the document Xiaofeng Zhuang et al.: “A novel approach for modeling cracking furnace severity”, Intelligent Control and Automation, 2004, WCICA 2004, Fifth World Congress on Hangzhou, China, 15-19 Jun. 2004, Piscataway, N.J., USA, IEEE, US, Bd. 1, 15. Jun. 2004 (2004 Jun. 15), pages 250-253, XP010729576, ISBN: 0-7803-8273-0, and in the document Min Han et al.: “Application of Neural Networks on Multivariate Time Series Modeling and Prediction”, American Control Conference, 2006, Minneapolis, Minn., USA, Jun. 14-16, 2006, Piscataway, N.J., USA, IEEE, 14. Jun. 2006 (2006-06-14), pages 3698-3703, XP010929375, ISBN: 1-4244-0209-3, the combination of a PCA (PCA=Principal Component Analysis) with a recurrent neuronal network modeling states which follow each other in time is described.
In the document Zhou et al.: “Fault detection and classification in chemical processes based on neural networks with feature extraction”, ISA Transactions, Instrument Society of America, Pittsburgh, US, Vol. 42, No. 4, October 2003 (2003 October), pages 651-664, XP005835325, ISSN: 0019-0578, a combination of a polynomial fitting with the modeling of states based on a neural network is described.